An ad hoc group of several hundred federal employees and others has convened online to advocate improved “knowledge management” in the federal government.
Knowledge management refers generally to the production, preservation and exchange of knowledge in such a way as to maximize the sharing of information and to optimize its use.
“Although most federal agencies have some knowledge management activities, there is no centralized federal resource for knowledge management. There is no government-sponsored support function for knowledge management. There is no clearinghouse for data on extant activities, no library of best practices, and no consulation available,” according to the new Federal Knowledge Management Initiative.
“Over 700 Federal employees, contractors, academicians and interested members of the public have mounted a campaign to enhance collaboration, knowledge and learning in the Federal Government.”
“The objective is to establish formal knowledge management across government,” said Neil Olonoff, an Army employee contractor who started the new initiative on a volunteer basis.
The initiative is developing its developing its policy proposals along with educational and promotional materials on a public wiki site hosted by NASA.
Americans are paying too much for almost everything, because the United States has long treated its trucking industry as an artifact to be preserved rather than as an opportunity for innovation.
These ideas aim to advance the detailed policy solutions needed to foster public trust and implement fairness in the adoption of AI across diverse domains, from healthcare and government benefits to rural access, education, and worker protections.
The evidence is clear: algorithmic pay-setting is established in app-based work, and payroll/timekeeping failures show how software can produce systemic wage harm at scale
While a few states have taken steps to implement decision-making mechanisms for certain AI systems, too many leaders are simply accepting narratives about AI’s purported public benefit at face value – jumping to the “how” of AI implementation before thoroughly vetting potential systems and deciding whether they are appropriate to use at all.